1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
News and Resources for Members of the IEEE Signal Processing Society
Vijayaraghavan Thirumalai (EPFL, Switzerland), “Distributed Compressed Representation of Correlated Image Sets”, Advisor: Prof. Pascal Frossard (2012)
Vision sensor networks and video cameras find widespread usage in several applications that rely on effective representation of scenes or analysis of 3D information. These systems usually acquire multiple images of the same 3D scene from different viewpoints or at different time instants. Therefore, these images are generally correlated through displacement of scene objects. Efficient compression techniques have to exploit this correlation in order to efficiently communicate the 3D scene information. Instead of joint encoding that requires communication between the cameras, in this thesis the author concentrates on distributed representation, where the captured images are encoded independently, but decoded jointly to exploit the correlation between images. One of the most important and challenging tasks relies in estimation of the underlying correlation from the compressed correlated images for effective reconstruction or analysis in the joint decoder.
This thesis focuses on developing efficient correlation estimation algorithms and joint representation of multiple correlated images captured by various sensing methodologies, e.g., planar, omnidirectional and compressive sensing sensors. The geometry of the 2D visual representation and the acquisition complexity vary for each sensor type. Therefore, people need to carefully consider the specific geometric nature of the captured images while developing distributed representation algorithms. In this thesis, the author proposes efficient distributed scene representation algorithms in different scene analysis and reconstruction scenarios.
© Copyright 2023 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.